Field of the Invention
The present invention relates to a technique for processing an image acquired by use of a magnetic resonance imaging (hereinafter, referred to as “MRI”) apparatus. In particular, the present invention relates to a technique of diffusion kurtosis imaging.
Description of the Related Art
An MRI apparatus is a medical image acquisition system that utilizes mainly nuclear magnetic resonance phenomenon of protons. The MRI apparatus is capable of imaging any cross section noninvasively, and acquiring morphological information, and in addition, information regarding vital functions such as blood flow and metabolic functions. One of important images obtained through the MRI is a diffusion weighted image (DWI). The diffusion weighted image is an image where self-diffusion of water molecules contained in body tissue is weighted. Upon imaging, in order to acquire a signal depending on the diffusion rate, MPG (Motion Probing Gradient) pulses are applied before and after a refocusing radio frequency (RF) pulse, and then echoes are obtained therefrom. Here, the MPG pulse induces reduction of signal intensity due to phase dispersion on a nuclear spin that moves randomly.
Since the nuclear spin that diffuses in the MPG pulse application direction causes the reduction of signal intensity due to the phase dispersion, controlling the MPG pulse application direction allows acquisition of diffusing information in any directions. In addition, a diffusion weighting degree may be adjusted by using diffusion factor (b-value) being a parameter relating to application intensity and application time of the MPG pulse. The higher is the b-value, an image with a higher degree of diffusion weighting is acquired.
There is a technique for measuring a spatial diffusion distribution of water molecules, referred to as DTI (Diffusion Tensor Imaging). In the DTI, it is assumed that the spatial diffusion distribution of water molecules follows a model of three-dimensional elliptic diffusion being a Gaussian distribution, and by calculating its FA (Fractional Anisotropy), a structure of white matter nerve tract is analyzed. Repeating a pulse sequence for acquiring a diffusion weighted image (DWI) with varying the MPG pulse application direction configures a pulse sequence of DTI.
In recent years, as a technique for weighting a restricted degree of diffusion movement by a cell membrane, subcellular organelle, or the like, diffusion kurtosis imaging (DKI) is suggested. The DKI assumes the spatial diffusion distribution of water molecules as a diffusion model of non-Gaussian distribution. It is expected that this technique allows capturing microstructural change along with tissue degeneration and/or cell proliferation, compared to the DTI that assumes the spatial diffusion distribution as a diffusion model of Gaussian distribution. Repeating the DTI pulse sequence with varying the b-value configures a pulse sequence of DKI.
Generally, in analyzing an image obtained through the DKI (hereinafter, referred to as “DKI analysis”), a non-linear least square fitting process is executed for each pixel, in the diffusion weighted images obtained through the pulse sequence with an identical MPG pulse application direction and various b-values, and a diffusion coefficient and a kurtosis coefficient are estimated as the diffusion-related parameter with respect to each MPG pulse application direction (e.g., see “Age-related non-Gaussian diffusion patterns in the prefrontal brain”, Falangola M F, Jensen J H, Babb J S, Hu C, Castellanos F X, Martino A D, Ferris S H, and Helpern J A, Journal of Magnetic Resonance Imaging 28, 2008, p. 1345-1350, hereinafter, referred to as “non-patent document 1”). Then, in order to depict a spatial distribution of each coefficient, for instance, components of the diffusion tensor and the kurtosis tensor are calculated so as to obtain the diffusion coefficient, the kurtosis coefficient, and the like, in a first primary component direction or in the direction perpendicular to the primary component.
In order to acquire a high-quality image within a short period of time in the DKI analysis, the challenge is to stabilize and speed-up the non-linear least square fitting in the diffusion-related parameter estimation. In the technique described in the non-patent document 1, a smoothing filter is applied to all over the diffusion weighted image, as a preprocessing, so as to stabilize the calculation. Therefore, the diffusion weighted image as a basis for the diffusion-related parameter estimation may undergo blurring, and this may have an influence on the quality of a resultant parameter image. As a general method for stabilizing the calculation, there is a constrained non-linear least square fitting. If it is constrained, however, an initial value is modified more than once, and it is necessary to repeat the calculation. Therefore, this may extend the processing time.
The present invention has been made in view of the situation described above, and an object of the present invention is to provide a technique for obtaining a high-quality image at high speed in the DKI analysis.